The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi

Automatic recognition of different types of acute leukaemia using peripheral blood cell images

[eng] Clinical pathologists have learned to identify morphological qualitative features to characterise the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious haematological diseases. A drawback of visual morphological analysis is that is time consuming, requires well-trained personnel and is prone to intra-observer variability, which is particularly true when dealing with blast cells. Indeed, subtle interclass morphological differences exist for leukaemia types, which turns into low specificity scores in the routine screening. They are well-known the difficulties that clinical pathologists have in the discrimination among different blasts and the subjectivity associated with their morphological recognition. The general objective of this thesis is the automatic recognition of different types of blast cells circulating in peripheral blood in acute leukaemia using digital image processing and machine learning techniques. In order to accomplish this objective, this thesis starts with a discrimination among normal mononuclear cells, reactive lymphocytes and three types of leukemic cells using traditional machine learning techniques and hand-crafted features obtained from cell segmentation. In the second part of the thesis, a new predictive system designed with two serially connected convolutional neural networks is developed for the diagnosis of acute leukaemia. This system was proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage. Furthermore, it was evaluated for its integration in a real-clinical setting. This thesis also contributes in advancing the state of the art of the automatic recognition of acute leukaemia by providing a more realistic approach which reflects the real-life complexity of acute leukaemia diagnosis

Diagnosis of haematological malignancies in the era of total laboratory automation: comparison of the Advia 2120 to immunophenotyping and morphology

A research report submitted to the Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Medicine in the branch of Haematology. Johannesburg, March 2015The incidence of leukaemia in South Africa is 2.5 per 100 000 and has increased due to HIV. Accurate and timeous diagnosis of leukaemia directly impacts success of patient treatment and consequent survival. Usually the Full Blood count (FBC), white blood cell (WBC) differential count and review of the peripheral blood smear alerts the clinician to the possibility of leukaemia. However the number of qualified and skilled technologists in peripheral and central laboratories is on a continual decline making the performance of the critical function of peripheral blood review a challenge. The Advia 2120 haematology analyser performs a WBC and differential count using principles of flow cytometry and the cytograms generated can be used to classify haematological malignancies through the Peroxidase and nuclear density analysis (PANDA) classification system. The presence of myeloperoxidase (MPO) activity in 3% or more leukaemic blasts confirms acute myeloid leukaemia, and enzyme activity can be detected by immunophenotypic analysis or conventional cytochemistry . Research on the comparison of the Advia 2120 and manual morphologic assessment in the classification of leukaemias is limited in the South African setting, where leukaemia often coincides with infection. The aim of this study was to determine if the FBC, differential count and cytogram assessment by the Advia 2120 using the PANDA classification is as reliable as morphologic assessment in the initial classification of haematological malignancies from peripheral blood samples when using flow cytometry as the gold standard.. 150 cases of confirmed leukaemia were collected. The diagnosis obtained from either PANDA analysis and/or morphological assessment was compared to the diagnosis obtained by immunophenotypic analysis. Secondly, the MPO activity obtained by the Advia peroxidase cytogram was compared to the MPO obtained by conventional methods of immunophenotypic analysis and/or cytochemistry. Using the PANDA analysis system, only 48% (72/150) of cases overall were accurately classified. The inaccuracy was 9.3% (14/150) and 42.7% of cases could not be classified. The positive predictive value (PPV) was 88%. The most significant finding was all of the acute Page | iv promyelocytic leukaemia (APL) cases (8/8) had a distinct pattern and were accurately classified on cytogram analysis alone. Accurate sub-classification of other types of acute myeloid leukaemia using PANDA analysis alone was inconsistent. However, the accuracy in classifying leukaemia was improved when the Advia cytogram was used in conjunction with morphological analysis, as 90% (135/150) of cases were accurately classified. The sensitivity and specificity of the peroxidase cytogram in evaluating myeloperoxidase (MPO) activity was 85% and 88.6% respectively. The agreement between cytogram peroxidase activity and the reference methods was 89.1% and the Cohen’s kappa was 76.9%. To the best of our knowledge, there is no data comparing peroxidase activity on the cytogram to other methods. In conclusion, it was shown that the routine use of the Advia cytograms in conjunction with the morphology findings provides invaluable information in the initial screening of leukaemia. In cases with indistinct morphology, the cytograms have the potential to aid in a provisional classification. The peroxidase activity from the cytogram could be used as a surrogate marker for myeloperoxidase activity in leukaemia. Moreover, a tentative diagnosis of an APL is possible by simple analysis of the cytogram resulting in earlier diagnosis which could be life-saving

New approaches to the management of adult acute lymphoblastic leukemia

Traditional treatment regimens for adult acute lymphoblastic leukemia, including allogeneic hematopoietic cell transplantation, result in an overall survival of about 40%, a figure hardly comparable with the extraordinary 80-90% cure rate currently reported in children. When translated to the adult setting, modern pediatric-type regimens improve the survival to about 60% in young adults. The addition of tyrosine kinase inhibitors for patients with Philadelphia chromosome positive disease and the measurement of minimal residual disease to guide risk stratification and post-remission approaches has led to further improvements in outcomes. Relapsed disease and treatment toxicity - sparing no patient but representing a major concern especially in the elderly - are the most critical current issues awaiting further therapeutic advancement. Recently, there has been considerable progress in understanding the disease biology, specifically the Philadelphia-like signature as well as other high-risk subgroups. In addition, there are several new agents that will undoubtedly contribute to further improvement in the current outcomes. The most promising agents are new the monoclonal antibodies, immunomodulators, and chimeric antigen receptor T cells and, to a lesser extent, several new drugs targeting key molecular pathways involved in leukemic cell growth and proliferation. This review examines the evidence supporting the increasing role of the new therapeutic tools and treatment options in different disease subgroups, including frontline and relapsed/refractory disease. It is now possible to define the best individual approach based on to the emerging concepts of precision medicine

Automatic detection and classification of leukaemia cells

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Today, there is a substantial number of software and research groups that focus on the development of image processing software to extract useful information from medical images, in order to assist and improve patient diagnosis. The work presented in this thesis is centred on processing of images of blood and bone marrow smears of patients suffering from leukaemia, a common type of cancer. In general, cancer is due to aberrant gene expression, which is caused by either mutations or epigenetic changes in DNA. Poor diet and unhealthy lifestyle may trigger or contribute to these changes, although the underlying mechanism is often unknown. Importantly, many cancer types including leukaemia are curable and patient survival and treatment can be improved, subject to prompt diagnosis. In particular, this study focuses on Acute Myeloid Leukaemia (AML), which can be of eight distinct types (M0 to M7), with the main objective to develop a methodology to automatically detect and classify leukaemia cells into one of the above types. The data was collected from the Department of Haematology, Universiti Sains Malaysia, in Malaysia. Three main methods, namely Cellular Automata, Heuristic Search and classification using Neural Networks are facilitated. In the case of Cellular Automata, an improved method based on the 8-neighbourhood and rules were developed to remove noise from images and estimate the radius of the potential blast cells contained in them. The proposed methodology selects the starting points, corresponding to potential blast cells, for the subsequent seeded heuristic search. The Seeded Heuristic employs a new fitness function for blast cell detection. Furthermore, the WEKA software is utilised for classification of blast cells and hence images, into AML subtypes. As a result accuracy of 97.22% was achieved in the classification of blasts into M3 and other AML subtypes. Finally, these algorithms are integrated into an automated system for image processing. In brief, the research presented in this thesis involves the use of advanced computational techniques for processing and classification of medical images, that is, images of blood samples from patients suffering from leukaemia.The Institute of Higher Education of Malaysia and the Universiti Sains Islam Malaysia (USIM)

DRGs in Transfusion Medicine and Hemotherapy in Germany

Patients requiring transfusion medicine and hemotherapy in an inpatient setting are incorporated into the German Diagnosis Related Groups (G-DRG) system in multiple ways. Different DRGs exist in Major Diagnostic Category 16 for patients that have been admitted for the treatment of a condition from the field of transfusion medicine. However, the reimbursement might be not cost covering for many cases, and efforts have to be intensified to find adequate definitions and prices. We believe that this can only be successful if health service research is intensified in this field. For patients requiring hemotherapy and transfusion medicine concomitant to the treatment of an underlying disease such as cancer, multiple systems exist to increase remuneration, among them the Patient Clinical Complexity Level (PCCL) and complex constellations to induce DRG splits. For direct reimbursement of high cost products, additional remuneration fees (Zusatzentgelte, ZE) are the most important. In addition, expensive innovations not reflected within the DRGs can be reimbursed after application and negotiation of the New Diagnostic and Treatment Methods (Neue Untersuchungs- und Behandlungsmethoden, NUB) system. The NUB system guarantees that medical progress is put rapidly into clinical practice and prevents financial issues from becoming a stumbling block for the use of innovative drugs and methods

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm subimages are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks

Intelligent techniques using molecular data analysis in leukaemia: an opportunity for personalized medicine support system

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient’s genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.Haneen Banjar, David Adelson, Fred Brown, and Naeem Chaudhr